Abstract
Support vector machine (SVM) is a typical classification method of machine learning. By this method, not only linear classification can be realized, but also nonlinear classification can be transformed into linear classification problem by kernel function. The selection of kernel function and its parameters directly affects the classification performance of support vector machine. In this paper, genetic algorithm is used to optimize the parameters of support vector machine. The classification performance of the optimized SVM is verified by simulation.
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Luo, Y. (2022). Research on Classification Method of Support Vector Machine Based on Genetic Algorithm Optimization. In: S. Shmaliy, Y., Abdelnaby Zekry, A. (eds) 6th International Technical Conference on Advances in Computing, Control and Industrial Engineering (CCIE 2021). CCIE 2021. Lecture Notes in Electrical Engineering, vol 920. Springer, Singapore. https://doi.org/10.1007/978-981-19-3927-3_40
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DOI: https://doi.org/10.1007/978-981-19-3927-3_40
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